bizrockman

import json
import os
import re
from typing import Any, Optional

import litellm
from litellm import completion, completion_cost
from litellm.caching.caching import Cache
from litellm.main import ModelResponse, Usage
from loguru import logger

bizrockman

#!/usr/bin/env python3
"""
tau2_summarizer.py
------------------
Summarize Tau²-Bench result files (JSON) for Dual-Control, Retail runs.

- Reads a Tau² result JSON (created via `--save-to ...`)
- Aggregates per-task token usage and durations
- Computes Pass@1, totals, and normalized "per interaction" metrics (Dual = 2 * tasks)
- Optionally computes costs if prices are provided (USD per 1M tokens for input/output)

bizrockman

import json
import os
import re
from typing import Any, Optional

import litellm
from litellm import completion, completion_cost
from litellm.caching.caching import Cache
from litellm.main import ModelResponse, Usage
from loguru import logger

bizrockman

{
  "openapi": "3.0.0",
  "info": {
    "title": "Context7 API",
    "version": "1.0.0",
    "description": "LLM-optimized interface for accessing Context7 developer documentation."
  },
  "servers": [
    {
      "url": "https://context7.com/api/v1"

bizrockman

from appwrite.client import Client
from appwrite.services.databases import Databases
from appwrite.services.storage import Storage
from appwrite.query import Query

...
app_database = Databases(appwrite_client)

result = database.list_documents(
            database_id=database_id,

bizrockman

from appwrite.client import Client
from dotenv import load_dotenv

def init_appwrite_client() -> Client:
    '''
    Init Appwrite client and return Databases object
    '''
    client = Client()
    client.set_endpoint(os.getenv("APPWRITE_ENDPOINT"))
    client.set_project(os.getenv("APPWRITE_PROJECT_ID"))

bizrockman

import os
from openai import OpenAI
import json
import re
import hashlib
import random
from tqdm import tqdm
from dotenv import load_dotenv

load_dotenv()

bizrockman

import os
from autogen import UserProxyAgent
from autogen.agentchat.contrib.gpt_assistant_agent import GPTAssistantAgent

os.environ["AUTOGEN_USE_DOCKER"] = "False"
config_list = [
        {
            "model": "gpt-4o",
            "api_key": "sk-..."
        }

bizrockman

import autogen
from autogen import AssistantAgent, runtime_logging

from typing import Dict, List, Optional
import requests

sys_msg = """You are a weather reporter who provides a short weather report for a given city.
Make your response short. ONLY the weather information. Noting more. NO excuses, that you can only talk about the 
weather. Do not make an introduction. Just start with the weather information.
If no city is provided, tell the user that you need a city name. But you will generate a weather report for the city

bizrockman

import os
import requests
from autogen import AssistantAgent, UserProxyAgent, ConversableAgent
import openai

os.environ["AUTOGEN_USE_DOCKER"] = "False"


def get_current_weather(city_name):
    weather_api_key = "<API KEY>"